Imputation of income on the LS

Chris White, Office for National Statistics

[Project number 20067]

Research using the ONS Longitudinal Study has indicated that socio-economic
inequalities in health have persisted since 1972. However, due to the
fact that income is not collected at census we have been unable to look
at the relationship between income and mortality, and instead, have used
measures such as social class or housing tenure.

Research in other countries suggests a strong relationship between income
and mortality, and in some cases between income inequality and mortality.
The relationship differs between westernised countries and is of interest.
Imputing income on the LS would go some way to estimating whether or not
we are in a similar situation to the US, or to Canada for example.

Imputing income would also enable researchers to determine if area variation
in the relationship between income, income inequality and mortality is
wholly explained by the distribution of income among individuals. This
will be made possible due to the fact that ward level imputed income will
be available from March 2003.

Income estimates have already been added to the Sample of Anonymised
Records. However this estimation, based on ward level estimates for households,
could be improved given the level of data available at the individual
level on the LS.

There are a number of other uses for an income estimate, including simply
trying to see if use of such an estimate better explains variations in
mortality than does social class. An income estimate could expand the
number of users of the LS.

This project aims to create an income estimate to add to the LS dataset
by:

1. Identifying an optimal donor dataset (s)
2. Building a predictive equation for income based on variables available
in the LS on this donor dataset.
3. Applying this equation to the LS.

This equation would produce an estimate of income, an estimate of the
random error and an estimate of the variation in this random error. Thus
achieving an income estimate and an estimate of its reliability as an
indicator.